Adaptive Network Sustainability and Defense Based on Artificial Bees Colony Optimization Algorithm for Nature Inspired Cyber Security

Cyber Defense is becoming a major issue for every organization to keep business continuity intact.The presented paper explores the effectiveness of a meta-heuristic optimization algorithm-Artificial Bees Colony Algorithm(ABC)as an Nature Inspired Cyber Security mechanism to achieve adaptive defense.It experiments on the Denial-Of-Service attack scenarios which involves limiting the traffic flow for each node.Businesses today have adapted their service distribution models to include the use of the Internet,allowing them to effectively manage and interact with their customer data.This shift has created an increased reliance on online services to store vast amounts of confidential customer data,meaning any disruption or outage of these services could be disastrous for the business,leaving them without the knowledge to serve their customers.Adversaries can exploit such an event to gain unauthorized access to the confidential data of the customers.The proposed algorithm utilizes an Adaptive Defense approach to continuously select nodes that could present characteristics of a probable malicious entity.For any changes in network parameters,the cluster of nodes is selected in the prepared solution set as a probable malicious node and the traffic rate with the ratio of packet delivery is managed with respect to the properties of normal nodes to deliver a disaster recovery plan for potential businesses.

Solar Power Plant Network Packet-Based Anomaly Detection System for Cybersecurity

As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration.Renewable energy is becoming increasingly important,with solar power accounting for the most significant proportion of renewables.As the scale and importance of solar energy have increased,cyber threats against solar power plants have also increased.So,we need an anomaly detection system that effectively detects cyber threats to solar power plants.However,as mentioned earlier,the existing solar power plant anomaly detection system monitors only operating information such as power generation,making it difficult to detect cyberattacks.To address this issue,in this paper,we propose a network packet-based anomaly detection system for the Programmable Logic Controller(PLC)of the inverter,an essential system of photovoltaic plants,to detect cyber threats.Cyberattacks and vulnerabilities in solar power plants were analyzed to identify cyber threats in solar power plants.The analysis shows that Denial of Service(DoS)and Manin-the-Middle(MitM)attacks are primarily carried out on inverters,aiming to disrupt solar plant operations.To develop an anomaly detection system,we performed preprocessing,such as correlation analysis and normalization for PLC network packets data and trained various machine learning-based classification models on such data.The Random Forest model showed the best performance with an accuracy of 97.36%.The proposed system can detect anomalies based on network packets,identify potential cyber threats that cannot be identified by the anomaly detection system currently in use in solar power plants,and enhance the security of solar plants.

Finite-time fault-tolerant control of teleoperating cyber physical system against faults

This paper studies a finite-time adaptive fractionalorder fault-tolerant control(FTC)scheme for the slave position tracking of the teleoperating cyber physical system(TCPS)with external disturbances and actuator faults.Based on the fractional Lyapunov stability theory and the finite-time stability theory,a fractional-order nonsingular fast terminal sliding mode(FONFTSM)control law is proposed to promote the tracking and fault tolerance performance of the considered system.Meanwhile,the adaptive fractional-order update laws are designed to cope with the unknown upper bounds of the unknown actuator faults and external disturbances.Furthermore,the finite-time stability of the closed-loop system is proved.Finally,comparison simulation results are also provided to show the validity and the advantages of the proposed techniques.

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cyber Threat Intelligence(CTI)can facilitate APT actors’profiling for an immediate response.However,it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature.Based on the Adversarial Tactics,Techniques and Common Knowledge(ATT&CK)of threat behavior description,this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network(HTN)and Graph Convolutional Network(GCN)to solve this issue.It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence.With the help of the Bidirectional EncoderRepresentation fromTransformers(BERT)pretraining model to analyze the contextual semantics of cyber threat intelligence,the task of threat behavior identification is transformed into a text classification task,which automatically extracts attack behavior in CTI,then identifies the malware and advanced threat actors.The experimental results show that F1 achieve 94.86%and 92.15%for the multi-label classification tasks of tactics and techniques.Extend the experiment to verify the method’s effectiveness in identifying the malware and threat actors in APT attacks.The F1 for malware and advanced threat actors identification task reached 98.45%and 99.48%,which are better than the benchmark model in the experiment and achieve state of the art.The model can effectivelymodel threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.

S-and T-Invariants in Cyber Net Systems

Cyber nets are also known as self modifying nets. Though proposed anddefined some 20 years ago, they have never been under thorough study eversince. The reason for this is simple: the nonlinear nature of such nets keeps themaway from applications of well developed methods known to the whole Petri NetSociety in the world. This paper attempts to make a start of studying cybernets in depth by proposing a way to define and to verify S-invariants and T-invariants in such nets. These invariants reflect important dynamic propertiesof cyber nets. Invariants in cyber nets play a role similar to loop invariantsproposed and studied by E.W. Dijkstra and D. Gries when cyber nets are usedfor program specification.

Chaotic Sandpiper Optimization Based Virtual Machine Scheduling for Cyber-Physical Systems

Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CPS offers huge processing and storage resources for CPS thatfinds helpful for a range of application areas.At the same time,with the massive development of applica-tions that exist in the CPS environment,the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effective-ness of the CC platform,virtualization technologies have been employed for resource management and the applications are executed via virtual machines(VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS,this paper focuses on the design of chaotic sandpi-per optimization based VM scheduling(CSPO-VMS)technique for energy effi-cient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory,which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm,the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives afitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique,a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

Intrusion Detection System(IDS)is a network security mechanism that analyses all users’and applications’traffic and detectsmalicious activities in real-time.The existing IDSmethods suffer fromlower accuracy and lack the required level of security to prevent sophisticated attacks.This problem can result in the system being vulnerable to attacks,which can lead to the loss of sensitive data and potential system failure.Therefore,this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification(LTH-CNN).Here,the Correlation Coefficient based Mayfly Optimization(CC-MA)algorithm is used to extract the input characteristics for the IDS from the input data.Then,the optimized features are utilized by the LTH-CNN,which returns the attacked and non-attacked data.After that,the attacked data is stored in the log file and non-attacked data is mapped to the cyber security and data security phases.To prevent the system from cyber-attack,the Source and Destination IP address is converted into a complex binary format named 1’s Complement Reverse Shift Right(CRSR),where,in the data security phase the sensed data is converted into an encrypted format using Senders Public key Exclusive OR Receivers Public Key-Elliptic Curve Cryptography(PXORP-ECC)Algorithm to improve the data security.TheNetwork Security Laboratory-Knowledge Discovery inDatabases(NSLKDD)dataset and real-time sensor are used to train and evaluate the proposed LTH-CNN.The suggested model is evaluated based on accuracy,sensitivity,and specificity,which outperformed the existing IDS methods,according to the results of the experiments.

Enhancing Cybersecurity Competency in the Kingdom of Saudi Arabia:A Fuzzy Decision-Making Approach

The Kingdom of Saudi Arabia(KSA)has achieved significant milestones in cybersecurity.KSA has maintained solid regulatorymechanisms to prevent,trace,and punish offenders to protect the interests of both individual users and organizations from the online threats of data poaching and pilferage.The widespread usage of Information Technology(IT)and IT Enable Services(ITES)reinforces securitymeasures.The constantly evolving cyber threats are a topic that is generating a lot of discussion.In this league,the present article enlists a broad perspective on how cybercrime is developing in KSA at present and also takes a look at some of the most significant attacks that have taken place in the region.The existing legislative framework and measures in the KSA are geared toward deterring criminal activity online.Different competency models have been devised to address the necessary cybercrime competencies in this context.The research specialists in this domain can benefit more by developing a master competency level for achieving optimum security.To address this research query,the present assessment uses the Fuzzy Decision-Making Trial and Evaluation Laboratory(Fuzzy-DMTAEL),Fuzzy Analytic Hierarchy Process(F.AHP),and Fuzzy TOPSIS methodology to achieve segment-wise competency development in cyber security policy.The similarities and differences between the three methods are also discussed.This cybersecurity analysis determined that the National Cyber Security Centre got the highest priority.The study concludes by perusing the challenges that still need to be examined and resolved in effectuating more credible and efficacious online security mechanisms to offer amoreempowered ITES-driven economy for SaudiArabia.Moreover,cybersecurity specialists and policymakers need to collate their efforts to protect the country’s digital assets in the era of overt and covert cyber warfare.

A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.

A Systematic Approach for Cyber Security in Vehicular Networks

Vehicular Networks (VANET) are the largest real-life paradigm of ad hoc networks which aim to ensure road safety and enhance drivers’ comfort. In VANET, the vehicles communicate or collaborate with each other and with adjacent infrastructure by exchanging significant messages, such as road accident warnings, steep-curve ahead warnings or traffic jam warnings. However, this communication and other assets involved are subject to major threats and provide numerous opportunities for attackers to launch several attacks and compromise security and privacy of vehicular users. This paper reviews the cyber security in VANET and proposes an asset-based approach for VANET security. Firstly, it identifies relevant assets in VANET. Secondly, it provides a detailed taxonomy of vulnerabilities and threats on these assets, and, lastly, it classifies the possible attacks in VANET and critically evaluates them.

NFT Security Matrix:Towards Modeling NFT Ecosystem Threat

Digital assets have boomed over the past few years with the emergence of Non-fungible Tokens(NFTs).To be specific,the total trading volume of digital assets reached an astounding$55.5 billion in 2022.Nevertheless,numerous security concerns have been raised by the rapid expansion of the NFT ecosystem.NFT holders are exposed to a plethora of scams and traps,putting their digital assets at risk of being lost.However,academic research on NFT security is scarce,and the security issues have aroused rare attention.In this study,the NFT ecological process is comprehensively explored.This process falls into five different stages encompassing the entire lifecycle of NFTs.Subsequently,the security issues regarding the respective stage are elaborated and analyzed in depth.A matrix model is proposed as a novel contribution to the categorization of NFT security issues.Diverse data are collected from social networks,the Ethereum blockchain,and NFT markets to substantiate our claims regarding the severity of security concerns in the NFT ecosystem.From this comprehensive dataset,nine key NFT security issues are identified from the matrix model and then subjected to qualitative and quantitative analysis.This study aims to shed light on the severity of NFT ecosystem security issues.The findings stress the need for increased attention and proactive measures to safeguard the NFT ecosystem.

Network Defense Decision-Making Based on Deep Reinforcement Learning and Dynamic Game Theory

Existing researches on cyber attackdefense analysis have typically adopted stochastic game theory to model the problem for solutions,but the assumption of complete rationality is used in modeling,ignoring the information opacity in practical attack and defense scenarios,and the model and method lack accuracy.To such problem,we investigate network defense policy methods under finite rationality constraints and propose network defense policy selection algorithm based on deep reinforcement learning.Based on graph theoretical methods,we transform the decision-making problem into a path optimization problem,and use a compression method based on service node to map the network state.On this basis,we improve the A3C algorithm and design the DefenseA3C defense policy selection algorithm with online learning capability.The experimental results show that the model and method proposed in this paper can stably converge to a better network state after training,which is faster and more stable than the original A3C algorithm.Compared with the existing typical approaches,Defense-A3C is verified its advancement.

Cybersecurity Risk Management through Behavior-Based Contextual Analysis of Online Logs

This paper studies cyber risk management by integrating contextual log analysis with User and Entity Behavior Analytics (UEBA). Leveraging Python scripting and PostgreSQL database management, the solution enriches log data with contextual and behavioral information from Linux system logs and semantic datasets. By incorporating Common Vulnerability Scoring System (CVSS) metrics and customized risk scoring algorithms, the system calculates Insider Threat scores to identify potential security breaches. The integration of contextual log analysis and UEBA [1] offers a proactive defense against insider threats, reducing false positives and prioritizing high-risk alerts.

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical components.The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of CPES.Since security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection.The EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion detection.Besides,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)model.The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects.The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

The Role of AI in Cyber Security: Safeguarding Digital Identity

This article signals the use of Artificial Intelligence (AI) in information security where its merits, downsides as well as unanticipated negative outcomes are noted. It considers AI based models that can strengthen or undermine infrastructural functions and organize the networks. In addition, the essay delves into AI’s role in Cyber security software development and the need for AI-resilient strategies that could anticipate and thwart AI-created vulnerabilities. The document also touched on the socioeconomic ramifications of the emergence of AI in Cyber security as well. Looking into AI and security literature, the report outlines benefits including made threat detection precision, extended security ops efficiency, and preventive security tasks. At the same time, it emphasizes the positive side of AI, but it also shows potential limitations such as data bias, lack of interpretability, ethical concerns, and security flaws. The work similarly focuses on the characterized of misuse and sophisticated cyberattacks. The research suggests ways to diminish AI-generating maleficence which comprise ethical AI development, robust safety measures and constant audits and updates. With regard to the AI application in Cyber security, there are both pros and cons in terms of socio-economic issues, for example, job displacement, economic growth and the change in the required workforce skills.

A Study on the Challenges of Human-Centric Cyber-Security and the Guarantee of Information Quality

Information security and quality management are often considered two different fields. However, organizations must be mindful of how software security may affect quality control. This paper examines and promotes methods through which secure software development processes can be integrated into the Systems Software Development Life-cycle (SDLC) to improve system quality. Cyber-security and quality assurance are both involved in reducing risk. Software security teams work to reduce security risks, whereas quality assurance teams work to decrease risks to quality. There is a need for clear standards, frameworks, processes, and procedures to be followed by organizations to ensure high-level quality while reducing security risks. This research uses a survey of industry professionals to help identify best practices for developing software with fewer defects from the early stages of the SDLC to improve both the quality and security of software. Results show that there is a need for better security awareness among all members of software development teams.

Cyber Deception Using NLP

Cyber security addresses the protection of information systems in cyberspace. These systems face multiple attacks on a daily basis, with the level of complication getting increasingly challenging. Despite the existence of multiple solutions, attackers are still quite successful at identifying vulnerabilities to exploit. This is why cyber deception is increasingly being used to divert attackers’ attention and, therefore, enhance the security of information systems. To be effective, deception environments need fake data. This is where Natural Language (NLP) Processing comes in. Many cyber security models have used NLP for vulnerability detection in information systems, email classification, fake citation detection, and many others. Although it is used for text generation, existing models seem to be unsuitable for data generation in a deception environment. Our goal is to use text generation in NLP to generate data in the deception context that will be used to build multi-level deception in information systems. Our model consists of three (3) components, including the connection component, the deception component, composed of several states in which an attacker may be, depending on whether he is malicious or not, and the text generation component. The text generation component considers as input the real data of the information system and allows the production of several texts as output, which are usable at different deception levels.

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